Practitioners continually strive for improved data transport accuracy and reliability. These may be especially difficult objectives when the data channel is subject to noise interference (and all are), large signal power variations, or multi-path fading. The latter two situations are often encountered in radio data communications systems.
Large signal power variations are encountered because radio signal path loss may vary over literally orders of magnitude. Additional variations are encountered due to Raleigh fading when the transmitting device and receiving device are in relative motion. Under these circumstances the receiving device will encounter a faded signal, a composite of two or more signals each of which has arrived at a receiving device location via a different path. While the literature is replete with analysis of the properties of faded signals a brief summary may serve the purposes here.
The paths, above, may have different path lengths, hence delays, different path losses, and different incident directions. The different path lengths, specifically path delays and thus phase differences, result in destructive or constructive addition of the incident signals. Different path losses mean different signal powers or amplitudes. Different incident directions mean a slightly different signal frequency due to well known Doppler effects. The net of all these properties is the receiving device will encounter a signal, a composite of all the incident signals, subject to periodic large reductions in signal power (fades) exhibiting rapid phase and small frequency variations during these fades.
Various techniques for addressing certain of these various problems have been developed. Among such techniques are encoding the data to be transported to allow for error correction at a decoder. One form of data encoding that has been developed and used is convolutional encoding, wherein the transmitted symbols depend not only on the data to be transported but also on previous data that has been transported. This technique works well in additive white noise situations and is readily adaptable to various specific transport environments. Furthermore an optimum decoder, at least for additive gaussian noise channels, is readily implemented. This decoder is variously known as a Viterbi or trellis type decoder.
Convolutional encoding, notwithstanding advantages, does have limitations and may not always adequately compensate for the conditions encountered during a fade, specifically the impact on a particular symbol. To address this, inner and outer codes have been proposed wherein the coding steps nearest the channel are selected for there ability to at least "mark" particular symbols where circumstances, such as a fade, dictate low confidence in the channel during the corresponding symbol time. This technique, relying on the properties of certain codes, is known as detecting an erasure and is one way of using confidence in the communications channel to improve data transport integrity.
Other approaches have been developed, to directly assess a confidence level in the channel. These rely on measuring particular properties of the received signal that may be peculiar to or result from a faded signal. They include measuring and associating a received signal strength indication (RSSI) (typically an average of the received signal power measured over some time frame) with each symbol and utilizing that information in the decoding process. This approach presupposes a one to one or strong correlation between the RSSI and actual reliability or quality of the channel, an assumption that may not always be warranted.
A different approach, having application in some circumstances detects small frequency variations in the signal and uses that information as a proxy for a fade and hence lesser confidence in the channel. This approach has it's own difficulties including distinguishing between desired and undesired variations in signal frequency, generally increased complexities, and it may not allow for a situation where the incident signals are constructively adding, thus implying increased confidence in the channel.
Clearly a need exists for a data decoder with a dynamic channel metric that resolves these inadequacies.